{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "883bbada",
   "metadata": {},
   "source": [
    "## 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b1707102",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c724ad4a",
   "metadata": {},
   "source": [
    "## 数据加载， pd.read_excel('./18级高一体测成绩汇总.xls')默认加载第一个工作表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89323496",
   "metadata": {},
   "outputs": [],
   "source": [
    "male = pd.read_excel('C:/Users/徐玉祥/Desktop/模块四作业数据/18级高一体测成绩汇总.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61625fcf",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4'16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3133</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3901</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4946</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3'44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3538</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4'23</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5'19</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7042</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3'25</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5755</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4'39</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5688</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别 男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0     1  男    4'13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1     1  男    4'16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男    4'09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男    4'21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男    3'44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男    4'23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男    5'19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男    3'25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男    4'39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男       0   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9726fb78",
   "metadata": {},
   "source": [
    "## 数据加载， pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name = 1)指定加载第二个工作表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7084d0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "female = pd.read_excel('C:/Users/徐玉祥/Desktop/模块四作业数据/18级高一体测成绩汇总.xls',sheet_name = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "41ce6a3a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16</td>\n",
       "      <td>48</td>\n",
       "      <td>3775</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>3683</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>3331</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>3701</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>3592</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>9.60</td>\n",
       "      <td>150.0</td>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "      <td>2255</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>2937</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>10.18</td>\n",
       "      <td>152.0</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>2592</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>9.67</td>\n",
       "      <td>165.0</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>1829</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>9.09</td>\n",
       "      <td>180.0</td>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>2962</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重  BMI\n",
       "0     1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3    0\n",
       "1     1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6    0\n",
       "2     1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0    0\n",
       "3     1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7    0\n",
       "4     1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "588  17  女    3.51   9.60  150.0    24   41  2255  158.0  49.0    0\n",
       "589  17  女    4.00  10.18  150.0    13   36  2937  161.0  55.7    0\n",
       "590  17  女    3.45  10.18  152.0    15   35  2592  165.0  48.6    0\n",
       "591  17  女    4.01   9.67  165.0    10   41  1829  154.0  43.6    0\n",
       "592  17  女    4.48   9.09  180.0    10   46  2962  162.0  55.3    0\n",
       "\n",
       "[593 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45857061",
   "metadata": {},
   "source": [
    "## 评分标准加载，pd.read_excel('./体侧成绩评分表.xls',header = [0,1])，header=[0,1]表示多层列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6dcfa23",
   "metadata": {},
   "outputs": [],
   "source": [
    "rule = pd.read_excel('C:/Users/徐玉祥/Desktop/模块四作业数据/体侧成绩评分表.xls',header = [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05dfeef5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4540</td>\n",
       "      <td>100</td>\n",
       "      <td>3150</td>\n",
       "      <td>100</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100</td>\n",
       "      <td>53</td>\n",
       "      <td>100</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>100</td>\n",
       "      <td>3'24\"</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420</td>\n",
       "      <td>95</td>\n",
       "      <td>3100</td>\n",
       "      <td>95</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>3'35\"</td>\n",
       "      <td>95</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300</td>\n",
       "      <td>90</td>\n",
       "      <td>3050</td>\n",
       "      <td>90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>90</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "      <td>90</td>\n",
       "      <td>3'40\"</td>\n",
       "      <td>90</td>\n",
       "      <td>3'36\"</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050</td>\n",
       "      <td>85</td>\n",
       "      <td>2900</td>\n",
       "      <td>85</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3'47\"</td>\n",
       "      <td>85</td>\n",
       "      <td>3'43\"</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800</td>\n",
       "      <td>80</td>\n",
       "      <td>2750</td>\n",
       "      <td>80</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>178</td>\n",
       "      <td>80</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>80</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>80</td>\n",
       "      <td>3'50\"</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3680</td>\n",
       "      <td>78</td>\n",
       "      <td>2650</td>\n",
       "      <td>78</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78</td>\n",
       "      <td>13.6</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>175</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>78</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3560</td>\n",
       "      <td>76</td>\n",
       "      <td>2550</td>\n",
       "      <td>76</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76</td>\n",
       "      <td>12.2</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>76</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3440</td>\n",
       "      <td>74</td>\n",
       "      <td>2450</td>\n",
       "      <td>74</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74</td>\n",
       "      <td>10.8</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>74</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3320</td>\n",
       "      <td>72</td>\n",
       "      <td>2350</td>\n",
       "      <td>72</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>72</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3200</td>\n",
       "      <td>70</td>\n",
       "      <td>2250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>163</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>70</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>70</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3080</td>\n",
       "      <td>68</td>\n",
       "      <td>2150</td>\n",
       "      <td>68</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68</td>\n",
       "      <td>6.6</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>68</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2960</td>\n",
       "      <td>66</td>\n",
       "      <td>2050</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>66</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2840</td>\n",
       "      <td>64</td>\n",
       "      <td>1950</td>\n",
       "      <td>64</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64</td>\n",
       "      <td>3.8</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>64</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>64</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2720</td>\n",
       "      <td>62</td>\n",
       "      <td>1850</td>\n",
       "      <td>62</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>151</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>62</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2600</td>\n",
       "      <td>60</td>\n",
       "      <td>1750</td>\n",
       "      <td>60</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>4'45\"</td>\n",
       "      <td>60</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2470</td>\n",
       "      <td>50</td>\n",
       "      <td>1710</td>\n",
       "      <td>50</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>5'05\"</td>\n",
       "      <td>50</td>\n",
       "      <td>4'50\"</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2340</td>\n",
       "      <td>40</td>\n",
       "      <td>1670</td>\n",
       "      <td>40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>5'25\"</td>\n",
       "      <td>40</td>\n",
       "      <td>5'00\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2210</td>\n",
       "      <td>30</td>\n",
       "      <td>1630</td>\n",
       "      <td>30</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>5'45\"</td>\n",
       "      <td>30</td>\n",
       "      <td>5'10\"</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080</td>\n",
       "      <td>20</td>\n",
       "      <td>1590</td>\n",
       "      <td>20</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6'05\"</td>\n",
       "      <td>20</td>\n",
       "      <td>5'20\"</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950</td>\n",
       "      <td>10</td>\n",
       "      <td>1550</td>\n",
       "      <td>10</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6'25\"</td>\n",
       "      <td>10</td>\n",
       "      <td>5'30\"</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    男肺活量       女肺活量      男50米跑      女50米跑       男体前屈       ...  女跳远       \\\n",
       "      成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩   分数  ...   成绩   分数   \n",
       "0   4540  100  3150  100   7.1  100   7.8  100  23.6  100  ...  204  100   \n",
       "1   4420   95  3100   95   7.2   95   7.9   95  21.5   95  ...  198   95   \n",
       "2   4300   90  3050   90   7.3   90   8.0   90  19.4   90  ...  192   90   \n",
       "3   4050   85  2900   85   7.4   85   8.3   85  17.2   85  ...  185   85   \n",
       "4   3800   80  2750   80   7.5   80   8.6   80  15.0   80  ...  178   80   \n",
       "5   3680   78  2650   78   7.7   78   8.8   78  13.6   78  ...  175   78   \n",
       "6   3560   76  2550   76   7.9   76   9.0   76  12.2   76  ...  172   76   \n",
       "7   3440   74  2450   74   8.1   74   9.2   74  10.8   74  ...  169   74   \n",
       "8   3320   72  2350   72   8.3   72   9.4   72   9.4   72  ...  166   72   \n",
       "9   3200   70  2250   70   8.5   70   9.6   70   8.0   70  ...  163   70   \n",
       "10  3080   68  2150   68   8.7   68   9.8   68   6.6   68  ...  160   68   \n",
       "11  2960   66  2050   66   8.9   66  10.0   66   5.2   66  ...  157   66   \n",
       "12  2840   64  1950   64   9.1   64  10.2   64   3.8   64  ...  154   64   \n",
       "13  2720   62  1850   62   9.3   62  10.4   62   2.4   62  ...  151   62   \n",
       "14  2600   60  1750   60   9.5   60  10.6   60   1.0   60  ...  148   60   \n",
       "15  2470   50  1710   50   9.7   50  10.8   50   0.0   50  ...  143   50   \n",
       "16  2340   40  1670   40   9.9   40  11.0   40  -1.0   40  ...  138   40   \n",
       "17  2210   30  1630   30  10.1   30  11.2   30  -2.0   30  ...  133   30   \n",
       "18  2080   20  1590   20  10.3   20  11.4   20  -3.0   20  ...  128   20   \n",
       "19  1950   10  1550   10  10.5   10  11.6   10  -4.0   10  ...  123   10   \n",
       "\n",
       "     男引体      女仰卧      男1000米跑      女800米跑       \n",
       "      成绩   分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0   16.0  100  53  100   3'30\"  100  3'24\"  100  \n",
       "1   15.0   95  51   95   3'35\"   95  3'30\"   95  \n",
       "2   14.0   90  49   90   3'40\"   90  3'36\"   90  \n",
       "3   13.0   85  46   85   3'47\"   85  3'43\"   85  \n",
       "4   12.0   80  43   80   3'55\"   80  3'50\"   80  \n",
       "5    NaN   78  41   78   4'00\"   78  3'55\"   78  \n",
       "6   11.0   76  39   76   4'05\"   76  4'00\"   76  \n",
       "7    NaN   74  37   74   4'10\"   74  4'05\"   74  \n",
       "8   10.0   72  35   72   4'15\"   72  4'10\"   72  \n",
       "9    NaN   70  33   70   4'20\"   70  4'15\"   70  \n",
       "10   9.0   68  31   68   4'25\"   68  4'20\"   68  \n",
       "11   NaN   66  29   66   4'30\"   66  4'25\"   66  \n",
       "12   8.0   64  27   64   4'35\"   64  4'30\"   64  \n",
       "13   NaN   62  25   62   4'40\"   62  4'35\"   62  \n",
       "14   7.0   60  23   60   4'45\"   60  4'40\"   60  \n",
       "15   6.0   50  21   50   5'05\"   50  4'50\"   50  \n",
       "16   5.0   40  19   40   5'25\"   40  5'00\"   40  \n",
       "17   4.0   30  17   30   5'45\"   30  5'10\"   30  \n",
       "18   3.0   20  15   20   6'05\"   20  5'20\"   20  \n",
       "19   2.0   10  13   10   6'25\"   10  5'30\"   10  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b6329c",
   "metadata": {},
   "source": [
    "## 数据类型转换"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f7a71c",
   "metadata": {},
   "source": [
    "### 男1000米跑，数据类型是str，并且是4’26这种形式，需要变成float类型的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f0ba29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# male['男1000米跑'] = male['男1000米跑'].str.capitalizereplace(\"'\",\".\")\n",
    "male['男1000米跑'] = male['男1000米跑'].str.replace(\"'\",\".\")\n",
    "male['男1000米跑'] = pd.to_numeric(male['男1000米跑'], errors='coerce').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e9fc4b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 477 entries, 0 to 476\n",
      "Data columns (total 11 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   班级       477 non-null    int64  \n",
      " 1   性别       477 non-null    object \n",
      " 2   男1000米跑  477 non-null    float64\n",
      " 3   男50米跑    477 non-null    float64\n",
      " 4   男跳远      477 non-null    float64\n",
      " 5   男体前屈     477 non-null    int64  \n",
      " 6   男引体      477 non-null    int64  \n",
      " 7   男肺活量     477 non-null    int64  \n",
      " 8   身高       477 non-null    float64\n",
      " 9   体重       477 non-null    float64\n",
      " 10  BMI      477 non-null    int64  \n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ],
   "source": [
    "male.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b9dd088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>身高</th>\n",
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       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2785</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3133</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3901</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4946</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3538</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7042</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5755</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5688</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0     1  男     4.13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1     1  男     4.16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男     4.09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男     4.21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男     3.44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..      ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男     4.23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男     5.19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男     3.25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男     4.39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男     0.00   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ccf5a382",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
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       "      <th>成绩</th>\n",
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       "      <td>4540</td>\n",
       "      <td>100</td>\n",
       "      <td>3150</td>\n",
       "      <td>100</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100</td>\n",
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       "      <td>100</td>\n",
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       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100</td>\n",
       "      <td>53</td>\n",
       "      <td>100</td>\n",
       "      <td>3.30\"</td>\n",
       "      <td>100</td>\n",
       "      <td>3.24\"</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420</td>\n",
       "      <td>95</td>\n",
       "      <td>3100</td>\n",
       "      <td>95</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>3.35\"</td>\n",
       "      <td>95</td>\n",
       "      <td>3.30\"</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300</td>\n",
       "      <td>90</td>\n",
       "      <td>3050</td>\n",
       "      <td>90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>90</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "      <td>90</td>\n",
       "      <td>3.40\"</td>\n",
       "      <td>90</td>\n",
       "      <td>3.36\"</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050</td>\n",
       "      <td>85</td>\n",
       "      <td>2900</td>\n",
       "      <td>85</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3.47\"</td>\n",
       "      <td>85</td>\n",
       "      <td>3.43\"</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800</td>\n",
       "      <td>80</td>\n",
       "      <td>2750</td>\n",
       "      <td>80</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>178</td>\n",
       "      <td>80</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>80</td>\n",
       "      <td>3.55\"</td>\n",
       "      <td>80</td>\n",
       "      <td>3.50\"</td>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3680</td>\n",
       "      <td>78</td>\n",
       "      <td>2650</td>\n",
       "      <td>78</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78</td>\n",
       "      <td>13.6</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>175</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>4.00\"</td>\n",
       "      <td>78</td>\n",
       "      <td>3.55\"</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3560</td>\n",
       "      <td>76</td>\n",
       "      <td>2550</td>\n",
       "      <td>76</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76</td>\n",
       "      <td>12.2</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "      <td>4.05\"</td>\n",
       "      <td>76</td>\n",
       "      <td>4.00\"</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3440</td>\n",
       "      <td>74</td>\n",
       "      <td>2450</td>\n",
       "      <td>74</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74</td>\n",
       "      <td>10.8</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>4.10\"</td>\n",
       "      <td>74</td>\n",
       "      <td>4.05\"</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3320</td>\n",
       "      <td>72</td>\n",
       "      <td>2350</td>\n",
       "      <td>72</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>4.15\"</td>\n",
       "      <td>72</td>\n",
       "      <td>4.10\"</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3200</td>\n",
       "      <td>70</td>\n",
       "      <td>2250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>163</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>70</td>\n",
       "      <td>4.20\"</td>\n",
       "      <td>70</td>\n",
       "      <td>4.15\"</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3080</td>\n",
       "      <td>68</td>\n",
       "      <td>2150</td>\n",
       "      <td>68</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68</td>\n",
       "      <td>6.6</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>4.25\"</td>\n",
       "      <td>68</td>\n",
       "      <td>4.20\"</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2960</td>\n",
       "      <td>66</td>\n",
       "      <td>2050</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>4.30\"</td>\n",
       "      <td>66</td>\n",
       "      <td>4.25\"</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2840</td>\n",
       "      <td>64</td>\n",
       "      <td>1950</td>\n",
       "      <td>64</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64</td>\n",
       "      <td>3.8</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>64</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>4.35\"</td>\n",
       "      <td>64</td>\n",
       "      <td>4.30\"</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2720</td>\n",
       "      <td>62</td>\n",
       "      <td>1850</td>\n",
       "      <td>62</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>151</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>4.40\"</td>\n",
       "      <td>62</td>\n",
       "      <td>4.35\"</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2600</td>\n",
       "      <td>60</td>\n",
       "      <td>1750</td>\n",
       "      <td>60</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>4.45\"</td>\n",
       "      <td>60</td>\n",
       "      <td>4.40\"</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2470</td>\n",
       "      <td>50</td>\n",
       "      <td>1710</td>\n",
       "      <td>50</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>5.05\"</td>\n",
       "      <td>50</td>\n",
       "      <td>4.50\"</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2340</td>\n",
       "      <td>40</td>\n",
       "      <td>1670</td>\n",
       "      <td>40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>5.25\"</td>\n",
       "      <td>40</td>\n",
       "      <td>5.00\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2210</td>\n",
       "      <td>30</td>\n",
       "      <td>1630</td>\n",
       "      <td>30</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>5.45\"</td>\n",
       "      <td>30</td>\n",
       "      <td>5.10\"</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080</td>\n",
       "      <td>20</td>\n",
       "      <td>1590</td>\n",
       "      <td>20</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6.05\"</td>\n",
       "      <td>20</td>\n",
       "      <td>5.20\"</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950</td>\n",
       "      <td>10</td>\n",
       "      <td>1550</td>\n",
       "      <td>10</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6.25\"</td>\n",
       "      <td>10</td>\n",
       "      <td>5.30\"</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    男肺活量       女肺活量      男50米跑      女50米跑       男体前屈       ...  女跳远       \\\n",
       "      成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩   分数  ...   成绩   分数   \n",
       "0   4540  100  3150  100   7.1  100   7.8  100  23.6  100  ...  204  100   \n",
       "1   4420   95  3100   95   7.2   95   7.9   95  21.5   95  ...  198   95   \n",
       "2   4300   90  3050   90   7.3   90   8.0   90  19.4   90  ...  192   90   \n",
       "3   4050   85  2900   85   7.4   85   8.3   85  17.2   85  ...  185   85   \n",
       "4   3800   80  2750   80   7.5   80   8.6   80  15.0   80  ...  178   80   \n",
       "5   3680   78  2650   78   7.7   78   8.8   78  13.6   78  ...  175   78   \n",
       "6   3560   76  2550   76   7.9   76   9.0   76  12.2   76  ...  172   76   \n",
       "7   3440   74  2450   74   8.1   74   9.2   74  10.8   74  ...  169   74   \n",
       "8   3320   72  2350   72   8.3   72   9.4   72   9.4   72  ...  166   72   \n",
       "9   3200   70  2250   70   8.5   70   9.6   70   8.0   70  ...  163   70   \n",
       "10  3080   68  2150   68   8.7   68   9.8   68   6.6   68  ...  160   68   \n",
       "11  2960   66  2050   66   8.9   66  10.0   66   5.2   66  ...  157   66   \n",
       "12  2840   64  1950   64   9.1   64  10.2   64   3.8   64  ...  154   64   \n",
       "13  2720   62  1850   62   9.3   62  10.4   62   2.4   62  ...  151   62   \n",
       "14  2600   60  1750   60   9.5   60  10.6   60   1.0   60  ...  148   60   \n",
       "15  2470   50  1710   50   9.7   50  10.8   50   0.0   50  ...  143   50   \n",
       "16  2340   40  1670   40   9.9   40  11.0   40  -1.0   40  ...  138   40   \n",
       "17  2210   30  1630   30  10.1   30  11.2   30  -2.0   30  ...  133   30   \n",
       "18  2080   20  1590   20  10.3   20  11.4   20  -3.0   20  ...  128   20   \n",
       "19  1950   10  1550   10  10.5   10  11.6   10  -4.0   10  ...  123   10   \n",
       "\n",
       "     男引体      女仰卧      男1000米跑      女800米跑       \n",
       "      成绩   分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0   16.0  100  53  100   3.30\"  100  3.24\"  100  \n",
       "1   15.0   95  51   95   3.35\"   95  3.30\"   95  \n",
       "2   14.0   90  49   90   3.40\"   90  3.36\"   90  \n",
       "3   13.0   85  46   85   3.47\"   85  3.43\"   85  \n",
       "4   12.0   80  43   80   3.55\"   80  3.50\"   80  \n",
       "5    NaN   78  41   78   4.00\"   78  3.55\"   78  \n",
       "6   11.0   76  39   76   4.05\"   76  4.00\"   76  \n",
       "7    NaN   74  37   74   4.10\"   74  4.05\"   74  \n",
       "8   10.0   72  35   72   4.15\"   72  4.10\"   72  \n",
       "9    NaN   70  33   70   4.20\"   70  4.15\"   70  \n",
       "10   9.0   68  31   68   4.25\"   68  4.20\"   68  \n",
       "11   NaN   66  29   66   4.30\"   66  4.25\"   66  \n",
       "12   8.0   64  27   64   4.35\"   64  4.30\"   64  \n",
       "13   NaN   62  25   62   4.40\"   62  4.35\"   62  \n",
       "14   7.0   60  23   60   4.45\"   60  4.40\"   60  \n",
       "15   6.0   50  21   50   5.05\"   50  4.50\"   50  \n",
       "16   5.0   40  19   40   5.25\"   40  5.00\"   40  \n",
       "17   4.0   30  17   30   5.45\"   30  5.10\"   30  \n",
       "18   3.0   20  15   20   6.05\"   20  5.20\"   20  \n",
       "19   2.0   10  13   10   6.25\"   10  5.30\"   10  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a6eb08",
   "metadata": {},
   "source": [
    "### 评分标准中男1000米跑和女800米跑的成绩都是4‘10’‘这种形式，需要转化为float类型值其他所有数值类型的值，都要转换为float类型的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "718d0526",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rule = rule.replace(\"'\",\".\",regex=True)\n",
    "ruleb= rule.replace('\"',\"\",regex=True)\n",
    "# rule = rule.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c0c9bb6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rule = ruleb.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6b1f2f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 24 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   (男肺活量, 成绩)     20 non-null     int64  \n",
      " 1   (男肺活量, 分数)     20 non-null     int64  \n",
      " 2   (女肺活量, 成绩)     20 non-null     int64  \n",
      " 3   (女肺活量, 分数)     20 non-null     int64  \n",
      " 4   (男50米跑, 成绩)    20 non-null     float64\n",
      " 5   (男50米跑, 分数)    20 non-null     int64  \n",
      " 6   (女50米跑, 成绩)    20 non-null     float64\n",
      " 7   (女50米跑, 分数)    20 non-null     int64  \n",
      " 8   (男体前屈, 成绩)     20 non-null     float64\n",
      " 9   (男体前屈, 分数)     20 non-null     int64  \n",
      " 10  (女体前屈, 成绩)     20 non-null     float64\n",
      " 11  (女体前屈, 分数)     20 non-null     int64  \n",
      " 12  (男跳远, 成绩)      20 non-null     int64  \n",
      " 13  (男跳远, 分数)      20 non-null     int64  \n",
      " 14  (女跳远, 成绩)      20 non-null     int64  \n",
      " 15  (女跳远, 分数)      20 non-null     int64  \n",
      " 16  (男引体, 成绩)      15 non-null     float64\n",
      " 17  (男引体, 分数)      20 non-null     int64  \n",
      " 18  (女仰卧, 成绩)      20 non-null     int64  \n",
      " 19  (女仰卧, 分数)      20 non-null     int64  \n",
      " 20  (男1000米跑, 成绩)  20 non-null     object \n",
      " 21  (男1000米跑, 分数)  20 non-null     int64  \n",
      " 22  (女800米跑, 成绩)   20 non-null     object \n",
      " 23  (女800米跑, 分数)   20 non-null     int64  \n",
      "dtypes: float64(5), int64(17), object(2)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "rule.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2051342",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4540.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>204.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.36</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3680.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3560.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>172.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3440.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.10</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3320.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>166.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.10</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>163.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3080.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2960.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>157.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2840.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2720.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2600.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2470.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2340.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>138.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2210.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>133.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.45</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>123.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      男肺活量           女肺活量        男50米跑        女50米跑         男体前屈         ...  \\\n",
       "        成绩     分数      成绩     分数    成绩     分数    成绩     分数    成绩     分数  ...   \n",
       "0   4540.0  100.0  3150.0  100.0   7.1  100.0   7.8  100.0  23.6  100.0  ...   \n",
       "1   4420.0   95.0  3100.0   95.0   7.2   95.0   7.9   95.0  21.5   95.0  ...   \n",
       "2   4300.0   90.0  3050.0   90.0   7.3   90.0   8.0   90.0  19.4   90.0  ...   \n",
       "3   4050.0   85.0  2900.0   85.0   7.4   85.0   8.3   85.0  17.2   85.0  ...   \n",
       "4   3800.0   80.0  2750.0   80.0   7.5   80.0   8.6   80.0  15.0   80.0  ...   \n",
       "5   3680.0   78.0  2650.0   78.0   7.7   78.0   8.8   78.0  13.6   78.0  ...   \n",
       "6   3560.0   76.0  2550.0   76.0   7.9   76.0   9.0   76.0  12.2   76.0  ...   \n",
       "7   3440.0   74.0  2450.0   74.0   8.1   74.0   9.2   74.0  10.8   74.0  ...   \n",
       "8   3320.0   72.0  2350.0   72.0   8.3   72.0   9.4   72.0   9.4   72.0  ...   \n",
       "9   3200.0   70.0  2250.0   70.0   8.5   70.0   9.6   70.0   8.0   70.0  ...   \n",
       "10  3080.0   68.0  2150.0   68.0   8.7   68.0   9.8   68.0   6.6   68.0  ...   \n",
       "11  2960.0   66.0  2050.0   66.0   8.9   66.0  10.0   66.0   5.2   66.0  ...   \n",
       "12  2840.0   64.0  1950.0   64.0   9.1   64.0  10.2   64.0   3.8   64.0  ...   \n",
       "13  2720.0   62.0  1850.0   62.0   9.3   62.0  10.4   62.0   2.4   62.0  ...   \n",
       "14  2600.0   60.0  1750.0   60.0   9.5   60.0  10.6   60.0   1.0   60.0  ...   \n",
       "15  2470.0   50.0  1710.0   50.0   9.7   50.0  10.8   50.0   0.0   50.0  ...   \n",
       "16  2340.0   40.0  1670.0   40.0   9.9   40.0  11.0   40.0  -1.0   40.0  ...   \n",
       "17  2210.0   30.0  1630.0   30.0  10.1   30.0  11.2   30.0  -2.0   30.0  ...   \n",
       "18  2080.0   20.0  1590.0   20.0  10.3   20.0  11.4   20.0  -3.0   20.0  ...   \n",
       "19  1950.0   10.0  1550.0   10.0  10.5   10.0  11.6   10.0  -4.0   10.0  ...   \n",
       "\n",
       "      女跳远          男引体          女仰卧        男1000米跑        女800米跑         \n",
       "       成绩     分数    成绩     分数    成绩     分数      成绩     分数     成绩     分数  \n",
       "0   204.0  100.0  16.0  100.0  53.0  100.0    3.30  100.0   3.24  100.0  \n",
       "1   198.0   95.0  15.0   95.0  51.0   95.0    3.35   95.0   3.30   95.0  \n",
       "2   192.0   90.0  14.0   90.0  49.0   90.0    3.40   90.0   3.36   90.0  \n",
       "3   185.0   85.0  13.0   85.0  46.0   85.0    3.47   85.0   3.43   85.0  \n",
       "4   178.0   80.0  12.0   80.0  43.0   80.0    3.55   80.0   3.50   80.0  \n",
       "5   175.0   78.0   NaN   78.0  41.0   78.0    4.00   78.0   3.55   78.0  \n",
       "6   172.0   76.0  11.0   76.0  39.0   76.0    4.05   76.0   4.00   76.0  \n",
       "7   169.0   74.0   NaN   74.0  37.0   74.0    4.10   74.0   4.05   74.0  \n",
       "8   166.0   72.0  10.0   72.0  35.0   72.0    4.15   72.0   4.10   72.0  \n",
       "9   163.0   70.0   NaN   70.0  33.0   70.0    4.20   70.0   4.15   70.0  \n",
       "10  160.0   68.0   9.0   68.0  31.0   68.0    4.25   68.0   4.20   68.0  \n",
       "11  157.0   66.0   NaN   66.0  29.0   66.0    4.30   66.0   4.25   66.0  \n",
       "12  154.0   64.0   8.0   64.0  27.0   64.0    4.35   64.0   4.30   64.0  \n",
       "13  151.0   62.0   NaN   62.0  25.0   62.0    4.40   62.0   4.35   62.0  \n",
       "14  148.0   60.0   7.0   60.0  23.0   60.0    4.45   60.0   4.40   60.0  \n",
       "15  143.0   50.0   6.0   50.0  21.0   50.0    5.05   50.0   4.50   50.0  \n",
       "16  138.0   40.0   5.0   40.0  19.0   40.0    5.25   40.0   5.00   40.0  \n",
       "17  133.0   30.0   4.0   30.0  17.0   30.0    5.45   30.0   5.10   30.0  \n",
       "18  128.0   20.0   3.0   20.0  15.0   20.0    6.05   20.0   5.20   20.0  \n",
       "19  123.0   10.0   2.0   10.0  13.0   10.0    6.25   10.0   5.30   10.0  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad07ffe",
   "metadata": {},
   "source": [
    "## 对体测成绩进行分数转换，跑步类（越小越好）；跳远、体前屈（越大越好 使用map、apply、transform方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fbc7795e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 男生成绩\n",
    "for col in ['男1000米跑', '男50米跑']:\n",
    "    score = rule[col]\n",
    "    def convert(x):\n",
    "        for i in range(len(score)):\n",
    "            if x <= score['成绩'].iloc[0]:\n",
    "                if x == 0:\n",
    "                    return 0\n",
    "                return 100\n",
    "            elif x > score['成绩'].iloc[-1]:\n",
    "                return 0 \n",
    "            elif (x > score['成绩'].iloc[i - 1]) and (x <= score['成绩'].iloc[i]):\n",
    "                return score['分数'].iloc[i]\n",
    "    \n",
    "    male[col + '成绩'] = male[col].map(convert)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "885b5b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['男跳远', '男体前屈', '男引体', '男肺活量']:\n",
    "    score = rule[col]\n",
    "    \n",
    "    def convert(x):\n",
    "        for i in range(len(score)):\n",
    "            if x >= score['成绩'].iloc[i]:\n",
    "                return score['分数'].iloc[i]\n",
    "        return 0\n",
    "    \n",
    "    male[col + '成绩'] = male[col].map(convert)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bea52bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['班级', '性别', '男1000米跑','男1000米跑成绩', '男50米跑','男50米跑成绩', '男跳远', '男跳远成绩', '男体前屈', '男体前屈成绩',\n",
    "                '男引体','男引体成绩', '男肺活量','男肺活量成绩' ,'身高', '体重', 'BMI']\n",
    "male = male[col]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8cebbe5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "male['BMI'] = (male['体重'] / (male['身高'] / 100) ** 2).round(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8699c2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 女生成绩\n",
    "for col in ['女800米跑', '女50米跑']:\n",
    "    score = rule[col]\n",
    "    def convert(x):\n",
    "        for i in range(len(score)):\n",
    "            if x <= score['成绩'].iloc[0]:\n",
    "                if x == 0:\n",
    "                    return 0\n",
    "                return 100\n",
    "            elif x > score['成绩'].iloc[-1]:\n",
    "                return 0 \n",
    "            elif (x > score['成绩'].iloc[i - 1]) and (x <= score['成绩'].iloc[i]):\n",
    "                return score['分数'].iloc[i]\n",
    "    \n",
    "    female[col + '成绩'] = female[col].map(convert)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f192e56e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['女跳远', '女体前屈', '女仰卧', '女肺活量']:\n",
    "    score = rule[col]\n",
    "    \n",
    "    def convert(x):\n",
    "        for i in range(len(score)):\n",
    "            if x >= score['成绩'].iloc[i]:\n",
    "                return score['分数'].iloc[i]\n",
    "        return 0\n",
    "    \n",
    "    female[col + '成绩'] = female[col].map(convert)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f89dc213",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['班级', '性别', '女800米跑','女800米跑成绩', '女50米跑','女50米跑成绩', '女跳远', '女跳远成绩', '女体前屈', '女体前屈成绩',\n",
    "                '女仰卧','女仰卧成绩', '女肺活量','女肺活量成绩' ,'身高', '体重', 'BMI']\n",
    "female = female[col]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ca809589",
   "metadata": {},
   "outputs": [],
   "source": [
    "female['BMI'] = (female['体重'] / (female['身高'] / 100) ** 2).round(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a9db4d46",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑成绩</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑成绩</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远成绩</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈成绩</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体成绩</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量成绩</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>11</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3133</td>\n",
       "      <td>68.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3538</td>\n",
       "      <td>74.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>15</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7042</td>\n",
       "      <td>100.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>13</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5755</td>\n",
       "      <td>100.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14</td>\n",
       "      <td>78.0</td>\n",
       "      <td>11</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5688</td>\n",
       "      <td>100.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑成绩  男50米跑  男50米跑成绩    男跳远  男跳远成绩  男体前屈  男体前屈成绩  \\\n",
       "0     1  男     4.13       72.0   8.88     66.0  195.0   60.0    12    74.0   \n",
       "1     1  男     4.16       70.0   7.70     78.0  225.0   74.0    11    74.0   \n",
       "2     1  男     4.09       74.0   8.45     70.0  218.0   70.0    14    78.0   \n",
       "3     1  男     4.21       68.0   8.05     74.0  206.0   64.0    13    76.0   \n",
       "4     1  男     3.44       85.0   7.52     78.0  210.0   66.0    13    76.0   \n",
       "..   .. ..      ...        ...    ...      ...    ...    ...   ...     ...   \n",
       "472  17  男     4.23       68.0   8.27     72.0  208.0   66.0    10    72.0   \n",
       "473  17  男     5.19       40.0   9.55     50.0  210.0   66.0    15    80.0   \n",
       "474  17  男     3.25      100.0   7.50     80.0  252.0   90.0    13    76.0   \n",
       "475  17  男     4.39       62.0   7.81     76.0  208.0   66.0    14    78.0   \n",
       "476  17  男     0.00        0.0   0.00      0.0    0.0    0.0     0    50.0   \n",
       "\n",
       "     男引体  男引体成绩  男肺活量  男肺活量成绩     身高    体重   BMI  \n",
       "0      1    0.0  2785    62.0  170.0  72.6  25.1  \n",
       "1      7   60.0  3133    68.0  174.0  52.7  17.4  \n",
       "2      1    0.0  3901    80.0  169.0  46.5  16.3  \n",
       "3      1    0.0  4946   100.0  183.0  79.7  23.8  \n",
       "4      9   68.0  3538    74.0  171.0  54.7  18.7  \n",
       "..   ...    ...   ...     ...    ...   ...   ...  \n",
       "472    0    0.0  4647   100.0  176.0  69.5  22.4  \n",
       "473    6   50.0  7042   100.0  177.0  76.0  24.3  \n",
       "474   13   85.0  5755   100.0  181.0  65.0  19.8  \n",
       "475   11   76.0  5688   100.0  172.0  51.7  17.5  \n",
       "476    0    0.0     0     0.0    0.0   0.0   NaN  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑成绩</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑成绩</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远成绩</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈成绩</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧成绩</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量成绩</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3775</td>\n",
       "      <td>100.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3683</td>\n",
       "      <td>100.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3331</td>\n",
       "      <td>100.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21</td>\n",
       "      <td>90.0</td>\n",
       "      <td>46</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3701</td>\n",
       "      <td>100.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8</td>\n",
       "      <td>64.0</td>\n",
       "      <td>34</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3592</td>\n",
       "      <td>100.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.9</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24</td>\n",
       "      <td>95.0</td>\n",
       "      <td>41</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2255</td>\n",
       "      <td>70.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.6</td>\n",
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       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13</td>\n",
       "      <td>72.0</td>\n",
       "      <td>36</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2937</td>\n",
       "      <td>85.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2592</td>\n",
       "      <td>76.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10</td>\n",
       "      <td>68.0</td>\n",
       "      <td>41</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1829</td>\n",
       "      <td>60.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10</td>\n",
       "      <td>68.0</td>\n",
       "      <td>46</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2962</td>\n",
       "      <td>85.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑成绩  女50米跑  女50米跑成绩    女跳远  女跳远成绩  女体前屈  女体前屈成绩  女仰卧  \\\n",
       "0     1  女    3.22     100.0   9.32     72.0  185.0   85.0    16    76.0   48   \n",
       "1     1  女    4.59      40.0  11.44     10.0  148.0   60.0     9    66.0   29   \n",
       "2     1  女    3.46      80.0  13.40      0.0  150.0   60.0     7    64.0   40   \n",
       "3     1  女    3.39      85.0   9.52     70.0  172.0   76.0    21    90.0   46   \n",
       "4     1  女    3.43      85.0   9.79     68.0  145.0   50.0     8    64.0   34   \n",
       "..   .. ..     ...       ...    ...      ...    ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51      78.0   9.60     70.0  150.0   60.0    24    95.0   41   \n",
       "589  17  女    4.00      76.0  10.18     64.0  150.0   60.0    13    72.0   36   \n",
       "590  17  女    3.45      80.0  10.18     64.0  152.0   62.0    15    76.0   35   \n",
       "591  17  女    4.01      74.0   9.67     68.0  165.0   70.0    10    68.0   41   \n",
       "592  17  女    4.48      50.0   9.09     74.0  180.0   80.0    10    68.0   46   \n",
       "\n",
       "     女仰卧成绩  女肺活量  女肺活量成绩     身高    体重   BMI  \n",
       "0     85.0  3775   100.0  163.0  51.3  19.3  \n",
       "1     66.0  3683   100.0  163.0  66.6  25.1  \n",
       "2     76.0  3331   100.0  157.0  60.0  24.3  \n",
       "3     85.0  3701   100.0  160.0  50.7  19.8  \n",
       "4     70.0  3592   100.0  167.0  63.9  22.9  \n",
       "..     ...   ...     ...    ...   ...   ...  \n",
       "588   78.0  2255    70.0  158.0  49.0  19.6  \n",
       "589   72.0  2937    85.0  161.0  55.7  21.5  \n",
       "590   72.0  2592    76.0  165.0  48.6  17.9  \n",
       "591   78.0  1829    60.0  154.0  43.6  18.4  \n",
       "592   85.0  2962    85.0  162.0  55.3  21.1  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(male,female)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bec4cba",
   "metadata": {},
   "source": [
    "## 对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d1b2d7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "male_1000 = pd.cut(male['男1000米跑成绩'],bins = 3)\n",
    "\n",
    "data = male_1000.value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "plt.rcParams['font.family']='KaiTi'\n",
    "plt.rcParams['font.size']=18\n",
    "\n",
    "_= plt.pie(data,autopct='%0.2f%%',labels=data.index,wedgeprops={'width':0.5},pctdistance=0.75)\n",
    "_= plt.title('    男1000米跑成绩    ',fontsize=32,pad=30,backgroundcolor='#c5b783',color='white',weight='bold')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9ed11124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_yt = pd.cut(male['男引体成绩'],bins = 3)\n",
    "\n",
    "data = male_yt.value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "_= plt.pie(data,autopct='%0.2f%%',labels=data.index,wedgeprops={'width':0.5},pctdistance=0.75)\n",
    "_= plt.title('    男引体成绩    ',fontsize=32,pad=30,backgroundcolor='#c5b783',color='white',weight='bold')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fbe3db0",
   "metadata": {},
   "source": [
    "## 对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c186e66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_800 = pd.qcut(female['女800米跑成绩'],q = 4,labels=['差','中','良','优'])\n",
    "\n",
    "data = female_800.value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "bars = plt.bar(x = data.index,height=data,color='#3c7f99',width=0.5)\n",
    "\n",
    "for i,bar in enumerate(bars):\n",
    "    h = bar.get_height()\n",
    "    w = bar.get_width()\n",
    "    plt.text(x = bar.get_x() + w/2,\n",
    "            y = h + 0.5,\n",
    "            s = data[i],\n",
    "            ha = 'center')\n",
    "    \n",
    "plt.box(False)\n",
    "plt.grid(axis='y',color='#3c7f99',alpha=0.35)\n",
    "\n",
    "_= plt.title(label='    女800米跑各分数段人数   ',\n",
    "         fontsize=32,\n",
    "         backgroundcolor='#c5b783',\n",
    "         color='white',\n",
    "         weight='bold',\n",
    "         pad=30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "97d320c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_ = pd.qcut(female['女跳远成绩'],q = 4,labels=['差','中','良','优'])\n",
    "\n",
    "data = female_.value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "bars = plt.bar(x = data.index,height=data,color='#3c7f99',width=0.5)\n",
    "\n",
    "for i,bar in enumerate(bars):\n",
    "    h = bar.get_height()\n",
    "    w = bar.get_width()\n",
    "    plt.text(x = bar.get_x() + w/2,\n",
    "            y = h + 0.5,\n",
    "            s = data[i],\n",
    "            ha = 'center')\n",
    "    \n",
    "plt.box(False)\n",
    "plt.grid(axis='y',color='#3c7f99',alpha=0.35)\n",
    "\n",
    "_= plt.title(label='    女跳远成绩各分数段人数   ',\n",
    "         fontsize=32,\n",
    "         backgroundcolor='#c5b783',\n",
    "         color='white',\n",
    "         weight='bold',\n",
    "         pad=30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50bfd2d8",
   "metadata": {},
   "source": [
    "## 使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖(男女生体重指数参考如下)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "253f1165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_BMI = pd.cut(male['BMI'],\n",
    "                    bins = [0,16.5,23.3,26.4,100],\n",
    "                    right = False,\n",
    "                    labels=['低体重','正常','超重','肥胖'])\n",
    "\n",
    "female_BMI = pd.cut(male['BMI'],\n",
    "                    bins = [0,16.5,22.8,25.3,100],\n",
    "                    right = False,\n",
    "                    labels=['低体重','正常','超重','肥胖'])\n",
    "\n",
    "data1 = male_BMI.value_counts()\n",
    "data2 = female_BMI.value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "plt.pie(data1,radius=1,\n",
    "       autopct='%0.2f%%',\n",
    "       pctdistance=0.85,\n",
    "       labels=data1.index,\n",
    "       wedgeprops={'linewidth':5,'width':0.3,'edgecolor':'white'},\n",
    "       textprops={'family':'KaiTi','fontsize':18})\n",
    "\n",
    "_=plt.pie(data2,radius=0.7,\n",
    "       autopct='%0.2f%%',\n",
    "       pctdistance=0.55,\n",
    "       wedgeprops={'linewidth':5,\n",
    "                  'width':0.7,\n",
    "                  'edgecolor':'white'})\n",
    "\n",
    "plt.rcParams['font.family']='KaiTi'\n",
    "plt.rcParams['font.size']=18\n",
    "\n",
    "_= plt.title(label='    男女生体重指数比例   ',\n",
    "         fontsize=32,\n",
    "         backgroundcolor='#c5b783',\n",
    "         color='white',\n",
    "         weight='bold',\n",
    "         pad=30)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "332adb25",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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